Homogenization of Chinese daily surface air temperatures :An update for CHHT1.0

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Homogenization of Chinese daily surface air temperatures :An update for CHHT1.0. Li Qingxiang , Xu Wenhui, Xiaolan Wang, and coauthors (National Meteorological Information Center, CMA, Email: liqx@cma.gov.cn ). - PowerPoint PPT Presentation

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Homogenization of Chinese daily surface air temperatures:An update for CHHT1.0

Li Qingxiang , Xu Wenhui, Xiaolan Wang, and coauthors(National Meteorological Information Center, CMA, Email: liqx@cma.gov.cn)

Inhomogenity exits in Chinese observational historic temperature data series due to stations relocation, changes of observations, calculation daily mean values, etc. One should paid careful attention on this when using the data set, we start to detect and adjust the discontinuities from about 20 years ago.

1/3Test in daily/monthly/ annual

scales

PMT-with Ref/ daily Ref

CHHT1.0(Dec, 2006)Recent changes

Adj daily seires

Climate extreme changesMean temperature changes

User needs

Homogenization of Chinese daily surface air temperatures:An update for CHHT1.0

Li Qingxiang , Xu Wenhui, Xiaolan Wang, and coauthors(National Meteorological Information Center, CMA, Email: liqx@cma.gov.cn)

The CHHT1.0 Dataset (1951–2004) consists of monthly and daily surface observations from all national stations in mainland China. CHHT 1.0 includes mean, maximum, and minimum temperature data; assessments of data quality; and gridded versions of the three temperature variables.

2/3Test in daily/monthly/ annual

scales

PMT-with Ref/ daily Ref

CHHT1.0(Dec, 2006)Recent changes

Adj daily seires

Climate extreme changesMean temperature changes

User needs

Homogenization of Chinese daily surface air temperatures:An update for CHHT1.0

Li Qingxiang , Xu Wenhui, Xiaolan Wang, and coauthors(National Meteorological Information Center, CMA, Email: liqx@cma.gov.cn)

Using both metadata and the penalized maximum t test with the first order autocorrelation being accounted for to detect changepoints, and using the quantile-matching algorithm to adjust the data time series to diminish non-climatic changes. Station relocation was found to be the main cause for non-climatic changes, followed by station automation. 3/3

Test in daily/monthly/ annual scales

PMT-with Ref/ daily Ref

CHHT1.0(Dec, 2006)Recent changes

Adj daily seires

Climate extreme changesMean temperature changes

User needs

Release of the China Homogenized Historical Temperature (CHHT1.0) (1951-2001)

4

Chinese Surface Air Temperature series over 50 years/ 100 years and its uncertainties

5

(Li and Li, 2007)

- 1. 5

- 1

- 0. 5

0

0. 5

1

1. 5

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Statistics for Stations relocations from 1951-2010

6

Stations numbers for Automatic observation starts during 2000-2010

Why update?

• Time duration : CHHT1.0, 1951-2004, now, 1951-2012• Metadata: more integrated, more density of stations;• Advances in techniques: annual, monthly to daily.(1 st generation to 2nd

generation) ;• Raw data updated : 2011 - 2012 , CMA’s special project on the basic

data, some missing, questionable data has been made up or corrected;(below)

• Requirement of data users : CHHT1.0 users, climate change researchers.

1950 1960 1970 1980 1990 2000 20100

20

40

60

80

100

120

1950 1960 1970 1980 1990 2000 20100.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Num

ber o

f sta

tions

Year

Tmax Tmin

Per

cent

age

of e

rron

eous

dat

a(%

)

Tmax Tmin

Daily series

Objective methodDaily series Monthly and annual

series

Subjective approach1.Metadata; 2.climate change; 3.comparason with different scales

Integrated

Discontinuities

metadata, discontinuities in monthly and annual series

No metadata, discontinuities in monthly and annual series

Temporal change of the discontinuities

最低气温

平均气温

Probability density function of all QM-adjustments applied to daily Tmax and Tmin time series as necessary (a-b), and of the QM-adjustments to daily Tmax and Tmin due to relocation (c-d) and automation (e-f)

Annual mean DTR (Tmax - Tmin)49% stations significant

decrease trends

-0.9 - -0.6 -0.6 - -0.3 -0.3 - -0.1 -0.1 - 0 0 - 0 .1 0.1 - 0 .3 0.3 - 0 .6 0.6 - 0 .9

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